Drugs in COVID-19 Clinical Trials: Predicting Transporter-Mediated Drug-Drug Interactions Using In Vitro Assays and Real-World Data

Sook Wah Yee, Bianca Vora, Tomiko Oskotsky, Ling Zou, Sebastian Jakobsen, Osatohanmwen J Enogieru, Megan L Koleske, Idit Kosti, Mattias Rödin, Marina Sirota, Kathleen M Giacomini

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Numerous drugs are currently under accelerated clinical investigation for the treatment of coronavirus disease 2019 (COVID-19); however, well-established safety and efficacy data for these drugs are limited. The goal of this study was to predict the potential of 25 small molecule drugs in clinical trials for COVID-19 to cause clinically relevant drug-drug interactions (DDIs), which could lead to potential adverse drug reactions (ADRs) with the use of concomitant medications. We focused on 11 transporters, which are targets for DDIs. In vitro potency studies in membrane vesicles or HEK293 cells expressing the transporters coupled with DDI risk assessment methods revealed that 20 of the 25 drugs met the criteria from regulatory authorities to trigger consideration of a DDI clinical trial. Analyses of real-world data from electronic health records, including a database representing nearly 120,000 patients with COVID-19, were consistent with several of the drugs causing transporter-mediated DDIs (e.g., sildenafil, chloroquine, and hydroxychloroquine). This study suggests that patients with COVID-19, who are often older and on various concomitant medications, should be carefully monitored for ADRs. Future clinical studies are needed to determine whether the drugs that are predicted to inhibit transporters at clinically relevant concentrations, actually result in DDIs.
Original languageEnglish
JournalClinical Pharmacology & Therapeutics
Volume110
Issue number1
Pages (from-to)108-122
ISSN0009-9236
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes

Keywords

  • COVID-19
  • Transporter inhibition
  • drug-drug interaction
  • real world data

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